Supply Chain Analytics
As of 2026-10-10
Supply chain serves the Auto/Industrial demand block (18%,) and moves working capital directly — frame everything in its KPIs (OTIF, forecast accuracy/MAPE, inventory turns, cash-to-cash). SAP IBP owns operational planning; Datasphere builds the cross-functional data products blending IBP plans + S/4HANA execution (MM/SD/PP) + external signals; SAC shows plan-vs-actual. Counter the bullwhip effect with demand sensing and one shared forecast; model at the planning granularity the business executes at, not the finest available. Control-tower analytics is the maturity destination (BDC's unified-foundation pattern). The premium pairs SAP fluency with genuine supply-chain literacy.
What you will learn
- Work through a realistic scenario: Industrial manufacturer, S/4HANA + IBP, poor forecast accuracy and rising inventory, CFO pushing working-capital reduction, wants a control tower.
- Recognize and avoid the anti-pattern: Framing analytics as data models, not supply-chain KPIs — Sponsor disengages; analytics seen as IT cost, not value.
- Apply the module's core decision: Where planning lives — choose IBP for planning; Datasphere/SAC for cross-domain analytics, not Using IBP analytics as the enterprise reporting layer.
- Track mastery with the KPI: KPI ladder (target: Every dashboard maps to OTIF/MAPE/turns/cash-to-cash; red flag: Dashboards measuring data, not business outcomes).
Module overview
Supply chain analytics serves the Automotive and Industrial demand block, one of the largest verticals in European SAP analytics work, and it is also the vertical where analytics most directly and most visibly moves money. A one-point improvement in forecast accuracy or in inventory turns shows up in working capital within a quarter — not within a strategic planning cycle two years out — which is precisely why supply-chain sponsors fund analytics work readily, on the condition that the consultant can speak their KPIs rather than talk about data models.
Prerequisites
- Intermediate hands-on experience on SAP analytics projects
- Review core concepts first: C043, C087, C083
Outcomes
- Work through a realistic scenario: Industrial manufacturer, S/4HANA + IBP, poor forecast accuracy and rising inventory, CFO pushing working-capital reduction, wants a control tower.
- Recognize and avoid the anti-pattern: Framing analytics as data models, not supply-chain KPIs — Sponsor disengages; analytics seen as IT cost, not value.
- Apply the module's core decision: Where planning lives — choose IBP for planning; Datasphere/SAC for cross-domain analytics, not Using IBP analytics as the enterprise reporting layer.
- Track mastery with the KPI: KPI ladder (target: Every dashboard maps to OTIF/MAPE/turns/cash-to-cash; red flag: Dashboards measuring data, not business outcomes).
Full module available to members. The full module adds: the decision framework · the end-to-end scenario walkthrough · the KPI scorecard · the anti-patterns · the code blocks · the knowledge check · the diagrams.